Abstract
Alzheimers Disease (AD) is the most common neurodegenerative disorder associated with aging. Early diagnosis of AD is key to the development, assessment, and monitoring of new treatments for AD. Machine learning approaches are increasingly being applied on the diagnosis of AD from structural MRI. However, the high feature-dimension and imbalanced data learning problem is two major challenges in the study of computer aided AD diagnosis. To circumvent this problem, we propose a novel formulation with hinge loss and sparse group lasso to select the discriminative features since features exhibit certain intrinsic group structures, then we propose a hybrid probabilistic oversampling to alleviate the class imbalanced distribution. Extensive experiments were conducted to compare this method against the baseline and the state-of-the-art methods, and the results illustrated that this proposed method is more effective for diagnosis of AD compared to commonly used techniques.
P. Cao–Supported in part by National Natural Science Foundation of China (61502091).
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References
Brookmeyer, R., Johnson, E., Ziegler-Graham, K.: Forecasting the global burden of Alzheimers disease. Alzheimer’s Dement. 3(3), 186–191 (2007)
Zhu, X., Suk, H., Shen, D.: Subspace regularized sparse multi-task learning for multi-class neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2015)
Peng, X., Lin, P., Zhang, T., Wang, J.: Extreme learning machine-based classification of ADHD using brain structural MRI data. PloS One 8(11), e79476 (2013)
Dubey, R., Zhou, J., Wang, Y., Thompson, P.M., Ye, J., et al.: Analysis of sampling techniques for imbalanced data: an n = 648 ADNI study. NeuroImage 87, 220–241 (2014)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Cao, P., Zhao, D., Zaiane, O.: An optimized cost-sensitive SVM for imbalanced data learning. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 280–292. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37456-2_24
Weiss, G.: The impact of small disjuncts on classifier learning. Ann. Inf. Syst. 5(8), 193–226 (2010)
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. B (Stat. Methodol.) 68(1), 49–67 (2006)
Beck, A.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2, 183–202 (2009)
Liu, F., Zhou, L., Shen, C., Yin, J.: Multiple Kernel learning in the primal for multimodal Alzheimers disease classification. IEEE J. Biomed. Health Inform. 18(3), 984–990 (2014)
Hinrichs, C., Singh, V., Peng, J., Johnson, S.: Q-mkl: matrix-induced regularization in multi-kernel learning with applications to neuroimaging. In: Advances in Neural Information Processing Systems, pp. 1421–1429 (2012)
Gu, B., Sheng, V.S.: A robust regularization path algorithm for \(\nu \)-support vector classification. IEEE Trans. Neural Netw. Learn. Syst. (99), 1–8 (2016)
Gu, B., Sheng, V.S., Wang, Z., Ho, D., Osman, S., Li, S.: Incremental learning for \(\nu \)-support vector regression. Neural Networks 67, 140–150 (2015)
Ye, J., Liu, J.: Sparse methods for biomedical data. ACM Sigkdd Explor. Newsl. 14(1), 4–15 (2012)
Maldonado, S., Weber, R., Famili, F.: Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines. Inf. Sci. 286, 228–246 (2014)
Yuan, L., Liu, J., Ye, J.: Efficient methods for overlapping group lasso. In: Advances in Neural Information Processing Systems, pp. 352–360 (2011)
Liu, J., Ye, J.: Moreau-Yosida regularization for grouped tree structure learning. In: Advances in Neural Information Processing Systems, pp. 1459–1467 (2010)
Figueiredo, M.A.T., Jain, A.K., Doi, K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381–396 (2002)
Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103(1), 127–152 (2005)
Wan, J., Zhang, Z., Rao, B.D., Fang, S., Yan, J., Saykin, A.J., Shen, L.: Identifying the neuroanatomical basis of cognitive impairment in Alzheimer’s disease by correlation-and nonlinearity-aware sparse Bayesian learning. IEEE Trans. Med. Imaging 33(7), 1475–1487 (2014)
Weiner, M.W., Aisen, P.S., Jack, C.R., Jagust, W.J., et al.: The Alzheimer’s disease neuroimaging initiative: progress report and future plans. Alzheimers Dement. 6, 202–211 (2010)
Ye, J., Farnum, M., Yang, E., Verbeeck, R., et al.: Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurol. 12(46), 1–12 (2012)
Lebedev, A.V., Westman, E., Van Westen, G.J.P., Kramberger, M.G., et al.: Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clin. 6, 115–125 (2014)
Acknowledgments
This research was supported by the National Natural Science Foundation of China (61502091), the Fundamental Research Funds for the Central Universities (N140403004), and the Postdoctoral Science Foundation of China (2015M570254).
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Cao, P., Liu, X., Zhao, D., Zaiane, O. (2017). Sparse Learning and Hybrid Probabilistic Oversampling for Alzheimer’s Disease Diagnosis. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_26
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